{
 "cells": [
  {
   "cell_type": "raw",
   "id": "00af9e8a-d7a0-4e98-8c9a-3d5f8b248746",
   "metadata": {},
   "source": [
    "# Make sure that Qiskit Aer is installed for noise models\n",
    "!pip install qiskit-aer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77f0e879",
   "metadata": {},
   "outputs": [],
   "source": [
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "\n",
    "import json\n",
    "import numpy.random as npr\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# mpl.rcParams[\"figure.dpi\"] = 300\n",
    "# plt.rcParams.update({\"text.usetex\": True, \"font.family\": \"Computer Modern Roman\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b8f526",
   "metadata": {},
   "source": [
    "# Recap: Qiskit IBM Runtime Estimator primitive workflow\n",
    "\n",
    "\n",
    "The Estimator primitive lets you calculate expectation values of quantum operators by specifying a list of circuits and observables. For a more detailed introductoin visit [this other tutorial](0-estimator.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb04ce9",
   "metadata": {},
   "source": [
    "## 1. Specify problem\n",
    "\n",
    "Let us first specify some example problem, i.e., a list of circuits and a list of observables that we want to measure on these circuits."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4203502b",
   "metadata": {},
   "source": [
    "### Define circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f003b651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1039.79x200.667 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1792.29x200.667 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 621.739x200.667 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit.circuit.library import TwoLocal\n",
    "from qiskit.circuit.random import random_circuit\n",
    "\n",
    "circuits = [\n",
    "    TwoLocal(2, rotation_blocks=[\"ry\", \"rz\"], entanglement_blocks=\"cz\").decompose(reps=1),\n",
    "    random_circuit(2, 4, seed=1).decompose(reps=1),\n",
    "    random_circuit(2, 2, seed=0).decompose(reps=1)\n",
    "]\n",
    "\n",
    "_ = [display(c.draw(\"mpl\")) for c in circuits]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b3f6e1",
   "metadata": {},
   "source": [
    "### Define observables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b4b5d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.quantum_info import SparsePauliOp\n",
    "\n",
    "observables = [\n",
    "    SparsePauliOp(\"ZZ\"),\n",
    "    SparsePauliOp(\"XX\"),\n",
    "    SparsePauliOp(\"YY\"),\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9619a704",
   "metadata": {},
   "source": [
    "## 2. Instantiate Estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5dc93c38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([ 0.90136719, -0.02734375, -0.00488281]), metadata=[{'variance': 0.18753719329833984, 'shots': 2048}, {'variance': 0.9992523193359375, 'shots': 2048}, {'variance': 0.9999761581420898, 'shots': 2048}])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.primitives import Estimator, BackendEstimator\n",
    "from qiskit.providers.fake_provider import FakeNairobi as Backend\n",
    "\n",
    "\n",
    "estimator = BackendEstimator(backend=Backend())\n",
    "\n",
    "# measure expectation values of observable-0 and 1 on circuit-0 (with specified parameters)\n",
    "job_0 = estimator.run(\n",
    "    circuits=[circuits[0]] * len(observables),\n",
    "    observables=observables,\n",
    "    parameter_values=[[*npr.random(16)]] * len(observables),\n",
    "    shots=2048,\n",
    ")\n",
    "\n",
    "# measure expectation values of observable-0 and 1 on circuit-1\n",
    "job_1 = estimator.run(\n",
    "    circuits=[circuits[1]] * len(observables),\n",
    "    observables=observables,\n",
    "    shots=2048,\n",
    ")\n",
    "\n",
    "result_1 = job_1.result()\n",
    "result_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "45602766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([ 0.90136719, -0.02734375, -0.00488281]), metadata=[{'variance': 0.18753719329833984, 'shots': 2048}, {'variance': 0.9992523193359375, 'shots': 2048}, {'variance': 0.9999761581420898, 'shots': 2048}])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_1  # print again for comparison on consecutive runs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4baad90e",
   "metadata": {},
   "source": [
    "# Zero noise extrapolation Estimator\n",
    "\n",
    "The `ZNEEstimator` extends `BackendEstimator` and performs zero noise extrapolation on top of it, taking the same arguments and being called through the same syntax. For a more detailed discussion visit [this other tutorial](1-zne.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "054e0d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne import zne\n",
    "ZNEEstimator = zne(BackendEstimator)  # Any implementation of BaseEstimator can be passed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a44ef1d-f684-4c3e-974c-6ef36609c2ba",
   "metadata": {},
   "source": [
    "## Specify ZNE strategy\n",
    "If you simply replace `BackendEstimator` --> `ZNEEstimator`, the conventional `BackendEstimator` primitive will be called (without performing ZNE).\n",
    "\n",
    "To perform zero noise extrapolation (ZNE), all we need is to provide a `ZNEStrategy` object when instantiating the Estimator. The `ZNEStrategy` takes in the following arguments:\n",
    "\n",
    "- **noise_factors**: A list of noise amplification factors.\n",
    "- **noise_amplifier**: The noise amplification strategy (e.g. whether to use global or local gate folding).\n",
    "- **extrapolator**: The extrapolation strategy (e.g. whether to use a linear or polynomial extrapolator)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e7ab293c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne import ZNEStrategy\n",
    "# from zne.noise_amplification import GlobalFoldingAmplifier, LocalFoldingAmplifier\n",
    "# from zne.extrapolation import LinearExtrapolator, PolynomialExtrapolator\n",
    "\n",
    "zne_strategy = ZNEStrategy(\n",
    "    noise_factors=(1, 3, 5),\n",
    "    # noise_amplifier = LocalFoldingAmplifier()\n",
    "    # extrapolator = LinearExtrapolator()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dcaa39c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EstimatorResult(values=array([ 0.93033854, -0.00252279,  0.0078125 ]), metadata=({'zne': {'noise_amplification': {'noise_amplifier': <CxAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>, 'noise_factors': (1, 3, 5), 'values': (0.9013671875, 0.841796875, 0.7841796875), 'variance': (0.18753719329833984, 0.2913780212402344, 0.38506221771240234), 'shots': (2048, 2048, 2048)}, 'extrapolation': {'extrapolator': LinearExtrapolator}}}, {'zne': {'noise_amplification': {'noise_amplifier': <CxAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>, 'noise_factors': (1, 3, 5), 'values': (-0.005859375, 0.02734375, 0.0126953125), 'variance': (0.9999656677246094, 0.9992523193359375, 0.9998388290405273), 'shots': (2048, 2048, 2048)}, 'extrapolation': {'extrapolator': LinearExtrapolator}}}, {'zne': {'noise_amplification': {'noise_amplifier': <CxAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>, 'noise_factors': (1, 3, 5), 'values': (0.013671875, -0.00390625, 0.013671875), 'variance': (0.9998130798339844, 0.9999847412109375, 0.9998130798339844), 'shots': (2048, 2048, 2048)}, 'extrapolation': {'extrapolator': LinearExtrapolator}}}))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator = ZNEEstimator(\n",
    "    backend=Backend(), \n",
    "    zne_strategy=zne_strategy  # !!!\n",
    ")\n",
    "\n",
    "# measure expectation values of observable-0 and 1 on circuit-0 (with specified parameters)\n",
    "job_0 = estimator.run(\n",
    "    circuits=[circuits[0]] * len(observables),\n",
    "    observables=observables,\n",
    "    parameter_values=[[*npr.random(16)]] * len(observables),\n",
    "    shots=2048,\n",
    ")\n",
    "\n",
    "# measure expectation values of observable-0 and 1 on circuit-1\n",
    "job_1 = estimator.run(\n",
    "    circuits=[circuits[1]] * len(observables),\n",
    "    observables=observables,\n",
    "    shots=2048,\n",
    ")\n",
    "\n",
    "result_1 = job_1.result()\n",
    "result_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b26b92e",
   "metadata": {},
   "source": [
    "## ZNE metadata\n",
    "Beautifying the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6814c600",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"values\": [\n",
      "    0.9303385416666667,\n",
      "    -0.0025227864583333304,\n",
      "    0.007812500000000002\n",
      "  ],\n",
      "  \"metadata\": [\n",
      "    {\n",
      "      \"zne\": {\n",
      "        \"noise_amplification\": {\n",
      "          \"noise_amplifier\": \"<CxAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
      "          \"noise_factors\": [\n",
      "            1,\n",
      "            3,\n",
      "            5\n",
      "          ],\n",
      "          \"values\": [\n",
      "            0.9013671875,\n",
      "            0.841796875,\n",
      "            0.7841796875\n",
      "          ],\n",
      "          \"variance\": [\n",
      "            0.18753719329833984,\n",
      "            0.2913780212402344,\n",
      "            0.38506221771240234\n",
      "          ],\n",
      "          \"shots\": [\n",
      "            2048,\n",
      "            2048,\n",
      "            2048\n",
      "          ]\n",
      "        },\n",
      "        \"extrapolation\": {\n",
      "          \"extrapolator\": \"LinearExtrapolator\"\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"zne\": {\n",
      "        \"noise_amplification\": {\n",
      "          \"noise_amplifier\": \"<CxAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
      "          \"noise_factors\": [\n",
      "            1,\n",
      "            3,\n",
      "            5\n",
      "          ],\n",
      "          \"values\": [\n",
      "            -0.005859375,\n",
      "            0.02734375,\n",
      "            0.0126953125\n",
      "          ],\n",
      "          \"variance\": [\n",
      "            0.9999656677246094,\n",
      "            0.9992523193359375,\n",
      "            0.9998388290405273\n",
      "          ],\n",
      "          \"shots\": [\n",
      "            2048,\n",
      "            2048,\n",
      "            2048\n",
      "          ]\n",
      "        },\n",
      "        \"extrapolation\": {\n",
      "          \"extrapolator\": \"LinearExtrapolator\"\n",
      "        }\n",
      "      }\n",
      "    },\n",
      "    {\n",
      "      \"zne\": {\n",
      "        \"noise_amplification\": {\n",
      "          \"noise_amplifier\": \"<CxAmplifier:{'noise_factor_relative_tolerance': 0.01, 'random_seed': None, 'sub_folding_option': 'from_first'}>\",\n",
      "          \"noise_factors\": [\n",
      "            1,\n",
      "            3,\n",
      "            5\n",
      "          ],\n",
      "          \"values\": [\n",
      "            0.013671875,\n",
      "            -0.00390625,\n",
      "            0.013671875\n",
      "          ],\n",
      "          \"variance\": [\n",
      "            0.9998130798339844,\n",
      "            0.9999847412109375,\n",
      "            0.9998130798339844\n",
      "          ],\n",
      "          \"shots\": [\n",
      "            2048,\n",
      "            2048,\n",
      "            2048\n",
      "          ]\n",
      "        },\n",
      "        \"extrapolation\": {\n",
      "          \"extrapolator\": \"LinearExtrapolator\"\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from zne.utils.serialization import EstimatorResultEncoder\n",
    "\n",
    "print(EstimatorResultEncoder.dumps(result_1, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acadd989",
   "metadata": {},
   "source": [
    "## Noise Amplifiers\n",
    "\n",
    "Currently only digital noise amplification is implemented:\n",
    "\n",
    "* `GlobalFoldingAmplifier`\n",
    "* `LocalFoldingAmplifier`\n",
    "    * `CxAmplifier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfaba56c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['CxAmplifier', 'LocalFoldingAmplifier', 'GlobalFoldingAmplifier'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from zne import NOISE_AMPLIFIER_LIBRARY\n",
    "NOISE_AMPLIFIER_LIBRARY.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d381ab3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne.noise_amplification import GlobalFoldingAmplifier, LocalFoldingAmplifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542a6d77",
   "metadata": {},
   "source": [
    "Each `NoiseAmplifier` has a method called `amplify_circuit_noise` which takes the original circuit and a noise factor as input and returns the corresponding noise amplified circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ce4bc68",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams[\"figure.dpi\"] = 300\n",
    "plt.rcParams.update({\"text.usetex\": True, \"font.family\": \"Computer Modern Roman\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b340b060",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2116.05x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_amplifier = GlobalFoldingAmplifier()\n",
    "\n",
    "noisy_circuit = noise_amplifier.amplify_circuit_noise(circuit=circuits[2], noise_factor=1)\n",
    "\n",
    "display(noisy_circuit.draw(\"mpl\", plot_barriers=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1feed61f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1865.22x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_amplifier = LocalFoldingAmplifier()\n",
    "\n",
    "noisy_circuit = noise_amplifier.amplify_circuit_noise(circuit=circuits[2], noise_factor=1)\n",
    "\n",
    "display(noisy_circuit.draw(\"mpl\", plot_barriers=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a162f93f",
   "metadata": {},
   "source": [
    "The FoldingAmplifiers take in the following optional arguments:\n",
    "\n",
    "- **gates_to_fold**: A list of `int`/`str` specifying which gates to fold.\n",
    "- **sub_folding_option**: `from_first`, `from_last`, `random`: Specifies how to fold the gates if the noise factor is not an odd integer.\n",
    "- **random_seed**: Used when performing random sub-folding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1584c1",
   "metadata": {},
   "source": [
    "### 1. Sub-foldings\n",
    "\n",
    "Whenever the noise factor is __not an odd integer__, we have to additionally fold sub-parts of the circuit to account for these intermediate noise factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3a93c08a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 6631.05x1354.5 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_amplifier = GlobalFoldingAmplifier(\n",
    "    sub_folding_option = \"from_first\",\n",
    "    # random_seed = 123,\n",
    ")\n",
    "\n",
    "noisy_circuit = noise_amplifier.amplify_circuit_noise(circuit=circuits[2], noise_factor=4)\n",
    "\n",
    "display(noisy_circuit.draw(\"mpl\", plot_barriers=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c2c8cbf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 6380.22x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_amplifier = LocalFoldingAmplifier(\n",
    "    sub_folding_option = \"from_first\",\n",
    "    # random_seed = 123,\n",
    ")\n",
    "\n",
    "noisy_circuit = noise_amplifier.amplify_circuit_noise(circuit=circuits[2], noise_factor=4)\n",
    "\n",
    "display(noisy_circuit.draw(\"mpl\", plot_barriers=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5495299",
   "metadata": {},
   "source": [
    "### 2. Folding only a specified set of gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b63be6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2868.55x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise_amplifier = LocalFoldingAmplifier(\n",
    "    gates_to_fold = 2,  # Two-qubit gates\n",
    "    # gates_to_fold = \"cx\",\n",
    "    # gates_to_fold = [\"cx\", \"rz\"],\n",
    "    # gates_to_fold = [2, \"rz\"],\n",
    ")\n",
    "\n",
    "noisy_circuit = noise_amplifier.amplify_circuit_noise(circuit=circuits[2], noise_factor=3)\n",
    "\n",
    "display(noisy_circuit.draw(\"mpl\", plot_barriers=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "996d4d92",
   "metadata": {},
   "source": [
    "__Note__: You can define custom instructions (e.g. `TwoLocal`, `RealAmplitudes`) and specify to fold those as well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b492394d",
   "metadata": {},
   "source": [
    "### Transpilation\n",
    "\n",
    "Currently noise amplification is performed __before__ transpilation. This will change soon!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9466a1d0",
   "metadata": {},
   "source": [
    "## Custom ZNE strategies\n",
    "\n",
    "The current implementation of `ZneEstimator` allows us build custom noise amplification, and extrapolation techniques:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4182d098",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne.extrapolation import Extrapolator, ReckoningResult\n",
    "from zne.noise_amplification import CircuitNoiseAmplifier\n",
    "\n",
    "\n",
    "############################  NOISE AMPLIFIER  ############################\n",
    "class CustomAmplifier(CircuitNoiseAmplifier):\n",
    "    def amplify_circuit_noise(self, circuit, noise_factor):\n",
    "        return circuit.copy()  # Dummy, nonperforming\n",
    "\n",
    "\n",
    "############################  EXTRAPOLATOR  ############################\n",
    "class CustomExtrapolator(Extrapolator):\n",
    "    @property\n",
    "    def min_points(self):\n",
    "        return 2\n",
    "    \n",
    "    def _extrapolate_zero(self, x_data, y_data, sigma_x, sigma_y):\n",
    "        value = 1.0\n",
    "        std_error = 1.0\n",
    "        metadata = {\"meta\": \"data\"}\n",
    "        return ReckoningResult(value, std_error, metadata)  # Dummy, nonperforming"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "833299e7",
   "metadata": {},
   "source": [
    "Which we then provide during instantiation through a `ZNEStrategy` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d29bb8dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"values\": [\n",
      "    1.0\n",
      "  ],\n",
      "  \"metadata\": [\n",
      "    {\n",
      "      \"zne\": {\n",
      "        \"noise_amplification\": {\n",
      "          \"noise_amplifier\": \"CustomAmplifier\",\n",
      "          \"noise_factors\": [\n",
      "            1,\n",
      "            3\n",
      "          ],\n",
      "          \"values\": [\n",
      "            0.0078125,\n",
      "            0.02734375\n",
      "          ],\n",
      "          \"variance\": [\n",
      "            0.99993896484375,\n",
      "            0.9992523193359375\n",
      "          ],\n",
      "          \"shots\": [\n",
      "            1024,\n",
      "            1024\n",
      "          ]\n",
      "        },\n",
      "        \"extrapolation\": {\n",
      "          \"extrapolator\": \"CustomExtrapolator\",\n",
      "          \"variance\": 0.0,\n",
      "          \"metadata\": null\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "zne_strategy = ZNEStrategy(\n",
    "    noise_amplifier=CustomAmplifier(),\n",
    "    noise_factors=(1, 3),\n",
    "    extrapolator=CustomExtrapolator(),\n",
    ")\n",
    "\n",
    "estimator = ZNEEstimator(backend=Backend(), zne_strategy=zne_strategy)\n",
    "    \n",
    "job = estimator.run(\n",
    "    circuits=circuits[1],\n",
    "    observables=observables[1],\n",
    ")\n",
    "    \n",
    "result = job.result()\n",
    "print(EstimatorResultEncoder.dumps(result, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66fad2c",
   "metadata": {},
   "source": [
    "## Full example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2701db75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H19Z7LaPP6ZsbWgAA0Lf0HzAwg088uegYfdaAgYMzcOiIomMA9Blf+/t3NkWdeMaUTi/u3d5SyupXl1fclZ3adGL92KIj9Gl+/vQk8wCHMnTEaUVH6NOG1vv51yIFDfQqc+fO7XB/5syZqa+vP+pxnnqq486gR1uysHDhwowfPz5NTU1pbm4+6vMD9Ha19AbCgIGDM2hofdExAIBeZGgNXQsltXXtBgAAAFDtau29llrLCwAAx6L/wCFFR+jzBvjfAKBH3L5wTra3lNrvf3HmXZ1+7qIn5idJhg8d2dWxqALm4mL5+dNTzAMcjt9DxfLzr022g6bXmD9/fkqlUofHrr322qMep7m5+aBxLrnkkorPaW5uzoIFC3L//fcf9HwAOqq1C8f+JwwuOgIA0IsMOKHGroVq7NoNAAAAoJrV2ntDtfZ3PQAAOBb9+59QdIQ+r18/yxoAutvyVU1ZtHR++/3PffjmTDxzSqeeu35Tc771yK1JkmGD67sjHgWrMxcXys+fnmAe4Ej69fd7qEheE9cm/6vRa8ybN++gxxobG496nKampg73GxoaUl9ff8hjL7744ixfvrz9fn19fT73uc/lqaee6vA41JJNLdvbb5d27ywwCb1aXV3RCY5KXV2/oiMAAL1InWshAAAAgD7Le0MAAFCFauw6vTfy2gOg+93+wJz222NHNuTGGQevwTmUx1cszF8vvLF9x3U7p/dOtfa+ZW/jWoieYB7gyMwDhTIP1CQFDfQKzc3NaW5u7vDYlClTDluscCSLFy/ucP9IJQ9z5pQvTEaOHJkpU6akoaEhSTJr1iwFDdSM5tKGJMnW3Tuz7PXm/N2KJe3fu23pd1I/aGguPq0hIwYNTZI01I8pJCe9y763dxUd4ajsfbul6AgAQC+y17UQAAAAQJ9Vc+8N7fHeEAAAvV/r3reLjtDnte7zvwFAd7pr0dys3/zOmpsPXTAzy1c1HfLY7btK2bZzc1avW5blq5s6PC9Jhg2p786oFKR1396iI/RproXobuYBKmltNQ8UyTxQmxQ00CssXLjwoMeuvfbaYxqrqanjxcX06dMPe+zs2bOP6RxQLZasfTYfW3Br6n9dvrDfgfe//Pi97bdLu3fmn2fdkmnjzu+xjPROu7ZvLjpCp7W27suelm1FxwAAepFauhZKkt07thQdAQAAAKDX8N4QAABUn3379hQdoc/bt9f/BgDd6YVXO25Aev+jt+X+R287prHsnN47tboeKlSrayG6mXmASvweKpaff21S0ECvsHjx4oMea2xsPOpxmpubUyqVjnscqBXTxp2fPV/6h6Jj0Ae9sfbnmTh1VtExOmXLa7/KvhrbyQgAqG5vvvxM0RGOyhsvPV10BAAAAIBe482Xf150hKOyca33hgAA6P327t6ZPbu2Z+DgYUVH6ZNa9+3Nrh21VWYHUGvmzT54zQ0caOfWDUVH6NN2vuXnT/cyD1CJeaBY5oHapKCBXqGpqemgx6ZMmXLc4zQ0NKS+vv5YY9W8CRMmZOXKlUXHqGjAtj3JA78qOgZV5Ac/eCh7hw8sOgZH8Mpbw/L95qJTdM5l55+RP62B34UAQG257xe789aeQUXH6JR/+tY3M7B/a9ExAAAAAHqFfa11+X+fbU1rW7+io1Q0ZMDbeeLxh1JXV3QSjmTb7hPyrV8WnYJq8dAPHsrwQW8XHQMAatJ9vzghb3XBhpVvbku+8d3jH+dIThmW3PDhIx9z96PJm9u7N8eb27pmnOGD27Ly2We7ZrA+xusBflNffE2wd+cJeeOxolNQLX7wg4cyYGjf+jfQVVa+MTI/ffX4x+lL10JJ110Pff0//mnOuf1fd81gNcjvcqpBX59DSrsGZkEXLc/sS3NBV80DH5/+wdz6h3177dqECROKjnDUFDRQ80ql0kGPHUs5Q5IsXtyxDaqxsfGYxuktBg8enPPOO6/oGBW1bd6evVHQwDsmTZqYupGarKvZuF2pmYKGyQ31Oe/d9UXHAAB6mQlbkqfXFp2islOGJxdd8J6iYwAAAAD0Kqe/kqzbUnSKysaPPiGTJ1f/Zwb6uk3bk1iQxa9NnDQpo3xcAgCOySmvJm+9cfzj7G1NXt96/OMcrze3V0eOzhg5/ISa+LxyNfJ6gN/UF18TtGyNRb20mzRpUoaMKDpFbXp7bbqkoMG10LE579yzc84pRacojt/lVIO+PofsejtdVtBgLjh6404fmfPOG1l0DI5S9dfxQwXNzQev8L3kkkuOaaympqYO96dPn35M4wBwZMMGlxf71YLxpxadAADojWrljym1khMAAACglpxTI39/qpWcAADQFUYMLTpB3+VnDwDFO2lI0Qn6thF+/kDBBp+QDBpQdIq+yzxQmxQ0UPM2b9580GPHUqzQ3NycUqnU4bHGxsZjjQVABZdNKDpBZafXJ2cpIAMAusHF5yQDauBdmQ/UwDUbAAAAQK2ZWgPvufSrSy5tKDoFAAD0nJEnFp2g7/KzB4DijRpWdIK+a0A/BRlAdRhpLiiMn31tqoGlAHBkv1mqkCQNDUf/KYGmpqaDxqivrz/GVABUMnVC0r/Kr0Qun5TU1RWdAgDojYYNTi4aV3SKIzttRNIwuugUAAAAAL3PWSOTcacUneLIJp+Z1NvFFgCAPsRGPsXxsweA4p00JBk+uOgUfdPpJ1f/ugqgb/DarDh+9rXJ9E3NO1SJwrEUNCxbtqzD/cbGxmONBEAnDB+cvO/solMc3qABySXnFJ0CAOjNLp9UdIIj+6CyKgAAAIBu88GJRSc4sg9W+XtXAADQ1c60GKIwZ40qOgEAUFdncWhRXIcC1cI8UIyRJyYnDio6BcdCQQM1b+TIg3/zH6q0oZL777+/w/3p06cfayQAOunKdyfVuubvtyYmg04oOgUA0JuNG5WMP7XoFIc2bFByyfiiUwAAAAD0Xu8bl9QPLTrFoZ1+cjJxTNEp6Kt27yx17Xg7unY8AKD3GjUsGTqw6BR9z8AByejhRaegWng9AFAsRQHFsCAaqBbmgWKYB2qXggZq3pQpU457jKamppRKpQ6PNTY2Hve4ABzZ2aOSK95ddIqDjRqW/PYFRacAAHq7urrk2qnJgCp8d2bmpclgZVUAAAAA3eaE/uX3hqpNv7rkusvK711BT9u9o5SNzU91eGxj81PHtajq5eeaDnps1dL7jnk8AKD3qquzGKUIZ5yc9KvCv5nT87weACje2aOKTtA3WZgLVIszTi7/nYie5b2I2jWg6ADQFaZMmZLly5e3329ubk5DQ0Onnz9nzpwO9xsaGlJfX99V8QA4ghkXJr94NXljW9FJ3nHdZckgV0kAQA84bUTysQuSB39edJJ3vO/s8hcAAAAA3es9pyeXTUieWFN0knc0nucDwfScp757W7ZuKP8D2PpGc15ZefDiqd07S/nbOSfnrMmNGXFq+bNAI8ZMyCWfuPmgY7dubM5TD85rv//Kc03ZurH5oOOWffe2vPDkwpx6zpQMPrH8f/hLrpmbEaM7/1kjAKB3mnRasur1olP0LeeeVnQCiuL1AED1aRhdXpjb2lZ0kr5j2KDk9PqiUwCUDRyQjD81WbOx6CR9y7lji07AsbL0kF5hzpw5HUoWjqagYfr06Wlu7vjmS2NjY5fmA+DwBg4oFyL818VJNbyX86Fzk3eNKToFANCXfPg9yTOvJC9vKjpJ+Q8+M99fdAoAAACAvuNTU5JfrU9KO4tOkoytTz4yuegU9CWrn7gvG19ankFD65Ok/T8PZWPzU+Xdc3eWMmJ0w2EXZK18ZH6HsQ435q7tm/PKyqbs3llKkpx9/nQLsgCATD4z+e7Pi07Rt0w+s+gEFMXrAYDqM3Rg+XP0Cqt6znvPSPr1KzoFwDsmn6mgoSeNGJKcqTi9ZilooFeYPXt25s6dm1KplCRZvHhxp0oW5syZk6amg9s2p0+f3tURATiChtHJZy5JHniq2BwTxySfvKjYDABA39O/X/KvPpTc8VCxH8Q/oX/yr65Ihg0uLgMAAABAXzNkYPKHVyR/05Ts3ltcjuGDkz/8UDKgf3EZ6Huu+0/LunS8syc35vN/Xw3bAgAAtWrMSckpw5M3txWdpG+oH5qccXLRKSiK1wMA1em8MxQ09CRlVUC1mXxG8p3lRafoO847I+lXV3QKjpWOJXqNBQsWtN+eP39+xeOnT5+e+fPnd3jeflOmTOnSbABU9qFzkxkXFnf+cackf3RleWEiAEBPO/nE5KZpyUkFlSPsL4mYMLqY8wMAAAD0ZWePSm78cDKwoL9TnTgo+eOrk1NPKub8AABQLerqyotR6BmTzyj/zAGA6qEwoOcM6JecO7boFAAdnXpSMtrfi3qMebe2KWig12hsbMzNN9+cJCmVSpk+ffohj1u4cGFOPvnkPPXUU1m2bFnq6+s7fL++vj4NDQ3dHReAQ5g+Ofmdi3v+vBPHJDddnQw+oefPDQCw3+iTkn/zkWTkiT173oEDkjlXJe/1QSMAAACAwrxrTPLH05IhA3v2vCOGJP9menK6XWsBACBJcuHZRSfoOy7wswaAqjNqWHLWyKJT9A3vOT0ZNKDoFAAHe5/Xaj1iyMBk4mlFp+B4KGigV5k3b17uvPPOJElTU1MmTJiQuXPnZv78+ZkzZ04mTJiQWbNmpbGxMS+++GKmTJmSBQsWdBijsbGxiOgA/NqH35380RXJ8B7YPbouyVXvSWZflQxSzgAAVIFThydf+GhyXg+VJZx+cvKFjySTvMEHAAAAULjxp5bfqzl7VM+cb9Jp5feiThvRM+cDAIBacM4pyen1Rafo/UafVN5YCQCoPr81segEfcPlk4pOAHBol01I6uqKTtH7XdqQnNC/6BQcDwUN9DqzZ8/Oli1bcvPNN6e+vj7z58/P3Llz89RTT2XmzJlZs2ZNFixYkPr6+iTlIocDTZ8+vYDUABzo/LOSP/1EcvE53XeOU4cn//YjyaemuKAFAKrLSUOSG65M/sUHum/HxH51yW+fn/y7j9o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      "text/plain": [
       "<Figure size 5376.88x602 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cir = circuits[1]\n",
    "display(cir.draw(\"mpl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a4a38ada-07b3-48ca-b89a-19d288abf575",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SparsePauliOp(['ZZ'],\n",
      "              coeffs=[1.+0.j])\n"
     ]
    }
   ],
   "source": [
    "obs = observables[0]\n",
    "print(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07745488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_factors = range(1,9)\n",
    "list(noise_factors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fb4c061f-a589-4553-91d0-3e7127bfac54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from zne.extrapolation import PolynomialExtrapolator\n",
    "\n",
    "## ZNE STRATEGY\n",
    "noise_amplifier = LocalFoldingAmplifier(\n",
    "    gates_to_fold=2,\n",
    "    sub_folding_option='from_first',\n",
    ")\n",
    "\n",
    "extrapolator = PolynomialExtrapolator(\n",
    "    degree=2,\n",
    ")\n",
    "\n",
    "zne_strategy = ZNEStrategy(\n",
    "    noise_factors=noise_factors,\n",
    "    noise_amplifier=noise_amplifier,\n",
    "    extrapolator=extrapolator,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "18d7c815-7ef3-46c6-a1a9-189cf5c9062e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## CALCULATION\n",
    "estimator = ZNEEstimator(backend=Backend(), zne_strategy=zne_strategy)\n",
    "    \n",
    "job = estimator.run(\n",
    "    circuits=cir,\n",
    "    observables=obs,\n",
    ")\n",
    "    \n",
    "result = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6b77e31e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1050 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################  DATA  ############################\n",
    "exact = Estimator().run(cir, obs).result().values[0]\n",
    "mitigated = result.values[0]\n",
    "noise_factors = result.metadata[0][\"zne\"][\"noise_amplification\"][\"noise_factors\"]\n",
    "noisy_values = result.metadata[0][\"zne\"][\"noise_amplification\"][\"values\"]\n",
    "\n",
    "############################  PLOT  ############################\n",
    "plt.rcParams[\"figure.figsize\"] = (5,3.5)\n",
    "plt.grid(which='major',axis='both')\n",
    "\n",
    "plt.plot([0, noise_factors[-1]], [exact, exact], \"--\", label=f\"Exact\", color=\"#000000\")\n",
    "plt.scatter(0, mitigated, label=f\"Mitigated\", marker=\"x\", color=\"#785ef0\")\n",
    "if noise_factors[0] == 1:\n",
    "    plt.scatter(\n",
    "        noise_factors[0], noisy_values[0], \n",
    "        label=f\"Unmitigated\", marker=\"s\", color=\"#dc267f\",\n",
    "    )\n",
    "plt.plot(\n",
    "    noise_factors, noisy_values, \n",
    "    label=f\"Amplified\", marker=\"o\", color=\"#dc267f\",\n",
    ")\n",
    "\n",
    "plt.title(\"Zero noise extrapolation\")\n",
    "plt.xlabel(\"Noise Factor ($n$)\")\n",
    "plt.ylabel(f\"Expectation Value ($\\langle ZZ \\\\rangle$)\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "817a3291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td><code>qiskit-terra</code></td><td>0.22.0</td></tr><tr><td><code>qiskit-aer</code></td><td>0.11.0</td></tr><tr><th>System information</th></tr><tr><td>Python version</td><td>3.10.5</td></tr><tr><td>Python compiler</td><td>Clang 13.1.6 (clang-1316.0.21.2.5)</td></tr><tr><td>Python build</td><td>main, Jun 23 2022 17:15:25</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>8</td></tr><tr><td>Memory (Gb)</td><td>32.0</td></tr><tr><td colspan='2'>Fri Oct 14 22:18:30 2022 EDT</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2022.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter  # pylint: disable=unused-import,wrong-import-order\n",
    "\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "zne",
   "language": "python",
   "name": "zne"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
